{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc5ab911-1251-4fc0-88eb-fd685a82a76b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 15937\n",
      "\n",
      "汇总结果：\n",
      "   区域    销量    占位 同比去年\n",
      "0  华北  2354  4500  12%\n",
      "1  华南  1902  4500  25%\n",
      "2  东北  3524  4500  16%\n",
      "3  西北  2698  4500  21%\n",
      "4  西南  2896  4500  18%\n",
      "5  华东  2563  4500  25%\n"
     ]
    },
    {
     "data": {
      "image/png": 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zoe+gaSbJqzHDuxmnNH3//hOdu4+JdHWr8EQiIQoVyGlxX9EieYyvb9x6CLFYhGReHlG2p1Kr8f37T/wOWaksYhKpf+82CAgMws7dx2K9mpk1bVvVNRmdE11JvIrE6nqxSSCF0ul0uHz1bozPy5olA7aunwl5yFQqPz9/dO4xNlo/8/g2bNRc3Lz1CF06NsLRYxdQuGAuBCuUUKs10Ot0EIlFEItFcHSQw9HRAWfOXTc5f+rE/iiYP7tZu5kzpgMAeCfzwPFDlgtonzh9FTMj1KEqViQPDu5ebPzaUM/LkEz6E6yIsuB4TAgggIODDI6ODnBylBtrUl24dAs16nW3cnaYLj3HxUt/4mrtikkmq7kJhUIIhYZEq1wmhdxBhiEjZmPT1kMm51WpVBIjh3ZB5kzpjNuCgoKxbNV2zFu4Eb/8AxLsHoiIiIiIiIB4TCIplSqUq9oeyxaOgUKhxNyFGywe9/LVe7TpOBw7N8+BSxJn7NuxwLjv9NlraNluKAKD/sRXtyK1Z99JXLt+H6uWTkCpEgUAAK2a10bWzOnRpPUgfP/+ExXLF0Ov7i2M5/QdNBXff/yKVvs6nR4vXr1D8B8lFAoFZHIZ8uTKAsAwUiYwKAjOTk7w8/NH2jQpcO/6nijbu3r9HirX7IwffobrOzk5QCqVGBNepUsVROUKxTFj8kCMGDsPy1buiOF3JPH07NocuXNlRnCwEkqVClqN1qxIdHihBaABwNPDDeNH94z0WIlYDIk0pOh4yL/ps9dYnJrm7e2Bg7sXwdPTDYAhSdK9zwT8/PUbWcJ9kI+Ko0NYjaCMGVIjqYszBAIBBEJDEWyxWGTok0QMqVSKz5+/4dmLt5G2t2vvcew/dBp+ny5bvXbS5MVNlpPPkC4lihfNF+nxcrks0v0PH1kvZi8UCuHk5AAnJwe4qDUxWso+Om0bCof/96e0pUub0uoxlu6zRdMaxtcKhRLrNu3HzLlr46VYOxERERERUWzE63Q2pVKF9l1GWf3gd+3GfZw6cxU1qpUxbvsdEIghI2YnSAIp1MdP31Czfg+MH9UT/XobilQXLpQbp4+uwcBhM7BqyQTjvWzaehAHDp+NdtsBgUHIX7SR8etSJQrg6P5lAIAJU5aa1PnxcHe12t6vX4ZRB77ffxq3ubu54svX7wCAtCFFqCUScaxG4ESH309/fPnyPdL9AoEAUpnEsFpYaNImGitsFSuaB/VqV4xVn9zdXTGgT9sYnbNm/V6LSaSvX3+gfNX2aN2iDgb0aYNFy7biyLELqFKpBHZvnRfjvh0/tNLqMQsWb8LIcQuiPCYmS8SHF6wwjOQKCgpG197jrR6fOqU3pk3qDwAmI2dCXb56FxlzVodWo4FarYE65L8aTfwljyyRSiVwdJBDIBDY9Dq28uHjVyhVYd9PkVBkTCjK5TI4yGWRjnT7HRCItev3YsHSLUweERERERFRorPJ6myRjUhwcnJA+9b10bdXKyT39jTZ55LEGZfObMLWHUewdOV2/Pv4pS26Zkan02H0hIW4eecRli8cCycnB2RInwp7ts03HvPq9QcMHj7bZn1QqcJqmmzedgjdek8wfv3w1j6kS5sSCqUhIRD+g6S7uyGJJBKJkCG9oV7K23efcP/BM5v08+Dhs+jVf3K8t6vT6fHnjwIKpWFqlFarM/zTWY4jsUhkHN2hUqnx/uMXi8cJIDCOZjH5wI7IkzIfP33DtFmrsGnrQXz89A2A6bQ6v5/++Pnrd6Tnp/D2Mk7pfPvuE/R6QCgUQCgUQiAwjDgJn2CLzjQ5rVYLnxzV4O8fgAzpU+Pmpe0AgO59JmD3vhNwkMshl8vM3nehtXLUGg32Hzxt9TrZs/kYX1uamqZSqRMlkaFSqeNUH83Lyx1urklidI5/QCC+fo2fe23TcThu3HoY4/PWb9qPUeMWcNoaERERERH9NWySRIooa5YM6NC2Ppo3qQG3kALTOp0Ou/edwPTZa1C/TkX06dkSSZyd0K51PbRrXQ/3Hz7DgUNncOLUZdx78Cxep8pYsv/gaXz48AU7Ns1GsmSm9Ym69hpn0xFS0S34CwBfwn2wTZHcC/8+fomMGVJDKjWM+Dlx6kq898/W2nYaEaPj06ZJgUe39wMA3rz9aJPCzuGLgavDJVSWrdyBqTMjH2F0YNcilC9rqAFVoXrHeEu6hBaU1uvCYkWv1yM4WBlp7ai4vGe02ujF5ILZw9G+Tf1YXycqo8cvxLxFG+PcTt8eLdG3V+sYnbN4+dZoFdC3pTPnrjOBREREREREfxWbJZEy+qRBzWpl0KhBFeTPG1bcV6vV4vDR85g6axUePnoOAJg2axXWbNiLIQPao0XTmkji7IQ8ubIgT64sGDWsK/78UeD+w6e4ffcx7t57gncfPuPrlx/4/NUXQUHB8dbnW3f+xbpN+zFkQAeT7f16tUb7rqPiVOg5voQfdRM6hS1vnmzGbSdP//eSSH87W6wSmBBCp8GJxSJUq1zK6vGpU3sbX0e3kPjvcEXc37z9BJXKfBocADg7OyJlimQADCO0LE2Xi3hcfCVuAwJj3g6njhEREREREZmLtySSo6MchQvmRrXKJVGtSilkypjWZP8v/wBs23kUy1Zux8tX783O//btBwYNm4WxExejSYOqaNu6Hgrmz2Fsu1iRvChWJK/JOfcfPkOZSm3jbZRSlUolMLCveX2dmtXL4si+ZWjccgC+h6tJlBi+f/+JoKBgODk5IHVIEqlUifwADMmOcxduJmb3/i9pddEfKRbf3N1ckT2bDwKD/kChUCFNuERPMi8PZM6YFiKxYZUvR0c53n/4gvcfDInG0ESQs5Mjdm6ZY5P+KcIlg1q0G4wHD59bPK5hvcpYt9IwFbJ1h+G4c++xxePq1q6ATWumGdpWxE/SNvxIsgrVOkQ6tSxLpnS4dWUnAMD/t21WhyQiIiIiIvovi1MSKX26lBjQpy0KFciF7NkyGJfiDqVWa3Du4k3s2PUP9h44Fa0PhUFBwVi7cR/WbtyHDOlToXrV0qhWuRRKFs9vnLIVqs+AKfGWQMqbJyvWrphkLKS9a+9xZEif2pjIKlQgJ44fXIFaDXri0+dv8XLN2Hr77hNyZM8InwyGOkilSxYEYFjBLSELk9uL6E7tsoUC+bNj73bLhbcnjOmFCWN6mWwbOXY+FizZbLJNrdbg1JmrVq/l7OxoXKkwusIX1d6ybmaUI5FCbVwzNcqRSJbajovYFCaP7D6IiIiIiIjsWZySSG/ffUaG9KmRO1dm4zaNRoPLV+/i0pU7UGu0+PHjFxwc5GjRtGasrqFQqLDv4Gm07zIS+fNlR4li+VGiWD5oNFrcuvNvXLpvlNEnDfZsnWdcOv6f4xfRqftYyGQSbFg1BVVDpgJlzpQO/xxYjtoNe+Ltu0/xcu3oEgqExtfPXrxBjuwZkTtnZmTP5oPMIcvPHzp6LkH7FBcCgQCpUiZDcLACSqUaao1hlS9b1r4SiQyrYsmkUjg4yqHTavH9xy+r50V3atffKuhPMBq3HGD1uOzZfHD9wrZYXyd9OutL2QPRW/IeiL/ve2za0SXi6DMiIiIiIqK/VZySSHq9HoOGz8L+nQtw5tx1nDh9BafPXMMv/wAUKZQbp46ujq9+Yvuuozh5+ipOnrY+oiImMvmkxeF9S4zFtC9evo3WHYdDq9Xizx8tmrYejCXzR6NF0xoAgAzpU+HArkWoUrtzvK3e5OjoYHzdslkttGxWy+wYmSxsFNa9B89Qr3ZFZPRJgxZNwpJzh478d5JI7m4ueHz3YJzbyZI5PQJ8r8fq3F17j6N9l1HGr1Mk94JAIDAsXa8yJLZsWQ9JKBRCJBJCIhHDQS6Dg4McHz99M0l63Lz1COWrtkdA4B+oVCqkTpUcR/YtBQAMHz0PBw6fgUgkhFwug6ODHJ+/fLdZfy2Ry6XG1yXKt4zWdLYyldpGazqbQCCIlz6KRELrB0UgESfImgNERERERET/KXH+pPT02Wtky1vbbLsynqeDKBTxP72kcMFc2L5xFry83AEAZ8/fQNPWA02m3Wm1WnTrPR5arQatW9QBAPhkSI39OxaiWp2ucVo9KW2aFEiZwgs/ojEaxsXF2fj62vX7AACxWIxOHRoat4XWwqHY2bV1LvLkyhLlMSOGdMaIIZ2j1d7LR0dj3IfUGSvAP1yx6l/+Abh5+5Hx6/DJjZ+//PHu/ecYXyM+yaRS6wfFklgsipd2hKKYtyOJMHXW1uRyWbzVgCIiIiIiIrIVm/25XakMG8GxfPUODBo2K8ZtHNm3FKVLFoRGo4nXaU4CgQD9e7fGiCFdIJMZPgQfP3UZLdsNtfhBTq/Xo0ffSRAIBGjV3JAwy5kjE9avmoIGzfrFuG+ZM6VFowYT0LBeJUyathzzF29CzgJ1ozwnfFLuxq2HCA5WwMFBDmcnQw2ZHXuOxagPie1PsAJzF2yASq2GSqWGWm0Y9aNUqazWsHF3d8XYEd0BAF+//cCUGSutXk8oFJhOZ3OQ4dnzt/FyL3+7pK5JYj1ay5rQ9w8AXD6zOYojw5w/uT5ax8nlslj1KSJxuCRSjWqlkSN7RovHhY5GBACHeLq2NZUrFseYEd2xdMV2bNl+OEGuSUREREREFFs2SyLFZ2FatVoTb23lyJ4R82YORfGi+YzbNm09iF79rRfp7tV/Cry83FG1UkkAQIVyRdG/d2vMmrcuRn2YMqGf8bVEIoFGo43RiBKFQokLl2+jSsUSAAClUoVde47HqA+JLThYiTETF8Xq3LRpUhiTSP7+AVizfk+89Klpq4EAAKVKDaVCCa1OB61WB13If6Nj344FKFemMAAgS+6a+ObrZ/E4gSCsRpNYJIKDoxyODnL8DojdqmA+GVLj1esPsTo3Ln7++o1nz99YPc7Z2REpUyQDYCgMH1lh7fA08fS+d3SQG18P6tc+Wue4u7vGy7UjU6JYPowd2QMliuUDAOTKkcmm1yMiIiIiIooPNksixWdh2vhYYj19upQYMaQLmjSsalyBTavVYsKUpZizYEP0+qHVom2nETh9dA1yZM+Iw0fPYdnKHVavW692RXTt1MRku06nw7ETl6wWw3Z1cUajBlWRNUt6DBkx27hdFW6k1/lLt+D309/4tadH0mgVjCZTHz5+jXMb4esZGZJQkScmw68+FtvkEQD06tYCHds3QIFijS0Wkfb/HYgipZpZbSdL5nQ4uHtxjK49ceoyTJy6zOpx4Wsite4wPNKaSLbg4Ci3flAEXp7uNugJUDB/DowY2sWYAA6VJUv6WLWXJ3cWLJg9AguWbMI/xy/izx9FPPSSiIiIiIjIMhsmkeJvRav4SEgJhUKkTuVtTCD9/PUbnbqPwfGTl2PUTlBQMNp0GoE50wejdcfhkY6SypA+FfbtWAifDKlNtgcEBmHL9sNYtmIHXrx6Z/FcdzdX1KhWGnVqlkf5skUgl8vwz/GLxv0VyhVFrRpljV8XK5IH7m6uxkTSwL7tkCyZB4aMmIUffv5m7dP/j0H92mHsyB4AgB5dmmHx8q1mx+j1enz6/M1qW25uLjG69twZQ9GgXiUEBQVDpVZDq4k8Yebs7Gh8vXHN1EhHIglFQoiEIsjlUjg5OaBOw15xXoVx8PBZGDw85tNp4yJiMe+SJfKjf+82xlGMod5/+IL5izdh3cZ9sbpO0cJ5UDB/DqxfOQVBQcHInKsGAgJjn5AkIiIiIiKKyn9iJFJ8JKRevf6AmvV7YPrkASheNC9adxiGN28/xaqtp89eo3bDXlHe4+s3H/Hz12/j1+8/fMHSFduwftN+s1EnEokYBfJlR/myRVGpfDEULJAD4nAFlJVKFe7efwIASO7tiWULxwAwfI+FQiGSODth+uQB6NxjLACgZvWyyJA+FapWLom8heszkfR/RCwJi4vhgzsjXdqUAAxTPtWauE3/iumKZBKJGO5urnB3i9nUr9A+R4dQGPOV1f4GeXNnM75es3wS0qczveeXr99j3oIN2LTtkMmItPCis6pc5QrFja+fPHvNBBIREREREdlUgoxEcknibDYiJzpiU1g3qmXB9Xo9hoyYDZFIFOdC3dFJks2Zvw4L547ElBkrsWb9HoujlrZtmImK5YuZ3atWq8W5Czexc88xHDx8Fv6/A+Hk5IBtG2chRXIvAMDk6StQvFg+VCpfDM0aV8fzF2/x4eNXZEifCgBw5+5jkwSSh7srRGIx/gQFQ6lSGev92IpAIIBYLDIUsnaUQ6/X4/v3nza73t8gfNJDGE9L1IcXvnZOaDLm46evaNd5JK6GrNoX1peYXd/JySFGx89duAELl26Br68f/gQrolxdLPx0tjKV2kY6nU0oFBpjxtnZET/8fsWoT3+D8mWLIHeuzMavwyeQHj95hdnz12HnnuMW33vh60DlzZ0VO3ZHXjDfJ0NqVCxfzPj13v0n49p1IiIiIiKiKNksiRT+A2zzJjXQvEmNeGnLmuiMpojPld6icuDwWVy98QDfvv2I9JiH/75AzephU9MeP3mFLdsPY+vOI/j6New8D3dX7No6DwXz5wAAnD57DbPmrUPqVN64en4Lkjg7YfTwbiZtz5izxuTryeP7omWzWrG+n7at6qJtq6hXkYvKoSPn0Lzt4FifH0oebkUwYTRGaySk8KNHbDGKpnnTmiZfX756Fy3bDbFYA0sczZFFEokYPhnSoEW4ti3VVgIAR0c5nJ2doNVo8PmLb7zX4NHpdFCpdFCp1AgIDIJYLIKzkyPkcikCAv9YnAZni2SdJV5e0auTFBysMI4SDPXs+RtMmbESu/ediPLcN+/CRkf26NoMyZJ5WJyK6OjogDo1y0EqlQAwTLPdsuNItPpHREREREQUWzZLIonFIusHRbctUfS7GX7JcZckzsiSKV289SMigUAAUcioCScnB9y68wjBwaajMaJKIAHA8tU70bVTExz55wJWrd2FG7cemh1TskR+LJo7Epl80gIAXrx8h3adR0Kn0+Hd+8+o36QPdmyeYzKt6MChM7hw6VY83OXfJ3yh5JhOwbK18Emk6ExHigmZTIpk4RIZe/afROceY6FSqS0eH52RfF07NcGsqYPMtvtGMmKsVfPamD0tLBGo1WqjtXpi+ITKicMrI01ShSeRiI01zACgVYdh2H/wtNlxLkmcrLYVGxnSp0L+fNnh7OSI1Km80b5NfeO+qLp/9fp9rF63B507NMLPX78xadpyrF63J1rJ61Onr+DzF1+kSO4FsViMZo2rR6uv02evhm8kKwESERERERHFF9slkcIlfg4ePoslK7bFuI3pkwcgT64skMkk0T4n/Afn2jXLoXbNcjG+bmzlLdIgxsus+/r6IVPO6pEWGi6YPwd2b5lnnGr0/sMX1G7Y06Te0rUbD1CtTlcsXzQW+fNmx5Onr9B30FSztj599sWde48RHKyEInQJ+yiKIceVMGT5eqlUAkdHB7z/+CVe2vX0cDO+Dr98+98g/Oif0FEi8UWpVKFekz64dm4Lzl+6hQ5dR0c5HdHRwXoSac36PejfuzVSpfQ2bvv8xRf7Dp6yeLxer8efPwoEBgZBpdZAo9FCq7NNDEklEshlUsjkUshlskgTTy4uzja5vkqlxqolEyCRmD8m/azUGRs5bj6kUgkmTltmMqLQmt8BQajdsBcmjO6JggVywjuZR6THarVaPH32BguWbMLmbYejfQ0iIiIiIqLYslkSSSYPGxH06cs3XLx8O8Zt+PsHAABEIhEcHGRmo3wscYhFHaXEFlkCCQBu3fkXxcu1wIrF4+Du5or6TftaXIr+8ZNXKFOpLVKmSIav335YHPUwYcpSTJiyNF77nhi8PMOSSE5OjlEcmfDCx19sanpZ4+vrh0Yt+uP+w2dW61kdPHIOjx6/jPJ9o1ZrsHP3cZQolg837zzCxUu3cfrcNQQFBVs8fuWaXVi5Zlec7iG+ubomsUm7Hz99w7ETl0xWQgQAhUKJvQcsJ9lCBQcr0av/5Fhd9+mz12ja2nx0GBERERERUWKzWRLJyTFmRXotqVGve4zPOfzPeSTxKhLna0eHQCCARCI2TmeLbApQXL1+8xHV63aDs5MjfoUk1iITnaXc/+u27TyKbTuPJnY3LJKHG/1jq4Tm7buWi1JHtH7T/mgdN3rCwrh0J9EltVESCQAuXL6FWjXKws/PH0+evca16/exedshPH3+xmbXJCIiIiIi+lsJnD0LWy9OQkRkhxwd5RAKhAgM+pPYXSEiIiIiIkp0f1dVYiKiv0h8rz5HRERERET0X/Z3rY9ORERERERERER/JSaRiIiIiIiIiIjIKiaRiIiIiIiIiIjIKiaRiIgSmJeXO1KlTJbY3SAiIiIiIoqReE0iNWlYFXnzZI1TGz26NkPVyiWNX1euWBy9urWwep5AIEDKFMli/c/TI6lZmxXKFUXv7lFfu3LF4pg7YyiyZsmApK5J0LJZzUiPdUvqgprVykTZXoli+TBtUn/IbbQ8fGQcHeUoXjQvUiT3srjf3c0VbVvVRUafNNFqb/TwbqhepVSk+2tULY0Xj44iV87M0WqvWuVSGD+6J5J7e0br+KhkzZweRQrljnR/+bJF0KBepWi3t3fHAvTv3QZischsX9o0KTB1Qj9kyZQuVn3923h7e6B/7zZwcAiLzwzpU2HWtEGoXLF4nNqOj+dHKJckTihftkic2mjfuh7y581usq1rpyaYMWVgnNoFgEb1K+PJvUMoXDBXnNuKDZFIBFcXZ0gkltdWqFmtDIYP7mwxpmNCIhFjz/b5mDimd6zbOHZwBRbOGYG0aVLEug2BQACRyPxe2repb/ZMLl40L6ZO7I90aVNG2aa3twe6dGyM7Nl8LO5PlTIZXJI4Rat/Tk4O0f4X2c/MkqSuSXD2+DqULlkw2ucQEREREUUl3lZnk8tlmD19CH7/DkShkk0QHKyMVTt9e7bCoaPncOzEJQBAlUol0bBuJSxatiXK89ySuuDp/UOxuiYA3Lj5ABWqdzTZVqZUQbRoWhMLl0Z+bScnR3Rq3xC79h5H2jTJsWT+aPz6FYDD/5w3O7ZV81qYMqEfuvYajy3bD4ec74BG9atg/ab9AIBsWX3Qs2tzjB6/EABQqEBOODjIcfHybej1epP2rp3fivTpUiEgMCjKexMJhfD0dEO9Jn1w6sxVi8ekS5sSxw+tRL/B07F63W6z/Z4eSbFo7ki06zwSL1+9j/J6ObJnxJABHbBj9zEcPX4RACAWiyAWi6FUqqDX6xEYFAzvZB7QarUADB/y5HIpBAKBxRWxatUoi+ZNamDV2t1wdnLE5zdno+zD4BGzsGzlDov7Rg3vihJF8yFjzuoW97dpWQd5cmXBnn0no7wGAFQsXwyVyhfDsRMXodcbPpyLREI4yGXw/x2Ipo2ro1f3Frh5+xF+/Q4EAAgFAjg4yPD5y3coFLF7nySWLh0aY8iADnBycsCkacsBAA4OcnTt2ARPn70xOTZH9ox48vQ1dDqd1XZj+/zw9vZA+rQpkTy5F9KmTo7MmdIhT+6syB2SnMxRoA6+fv0Rs5uEIWk6cVwfaNQaFCrVFN+//wQAlC5ZACWL5ceQEbONx4rFImT0SYunz14bt8lkUri6JkFgYBCUSrUxzkOlTukNpVKFO/cem11bIBBAKpVAKBQYvw+ZM6bF7au7YnwfoTZvO4RuvScYvy5UIAdOHlmN3v0nY13Isye8qpVLon2b+pg5d02srwkAarUGlSsUhyKW/z+oWrkkShTLB5FQiE+fv8W6H1Mm9EOPLk3h6l3MZHupEgVQtXJJlK7YGq/ffAQA1KpeFp3aNcC2nUfw9l3kbaZMngyzpw1Gt97j8fjJK5N9MpkUWzfMhJOjA+o16YP3H75E2k5yb088f3gEQUHBUGs0Ud5HUtckmDh1GWbMsf5zcXCQYcv6GXB3c8Xbd5+sHk9EREREFB3xlkTq0LY+kromQdee42KdQAIMHzrU6rBfpFVKFVRqtdXzFErDNVev24PV682TIJERCoUQCYVQqc1/eY/YF0t8ff0AGJYCv3TlDrbtPIqUFqapyOUy9OreAkf+OY9tO49CJjMkTKpVLoVFc0dCJBJhzfo9xqRC6HXHj+6JnNkzmXyQDfUnWIGtO46g3+BpUfbRJ0Nq3Lu+B0qlKtJjgoMNiRu/n/4W94d+uFFH42fRsF5lqNUajJ24yLitWuVS2Lphptmx1y9sM/n6n+MX0bjlAJNtUqkENauVwc49x/D+wxfjiIJxkxbj4OGzZm1eu7A1yg+tf/4oI71PwPC9V6ms36dAIMCEMb0AADOnDMLMKYNM9vvkqIbO7RsCANatnGx2foVqHXDj1kOr1/lbZEifCr27t8C16/cxZcZK4/bQuAqNIQBo1rg6Fsweji3brccnEPvnR42qZbBg9nAAhveil5c7jp24iLETF+PN24+IkHeNtvGje8LVxRkNmvU1ed8pFCr8CXefXl7uWLt8EnLlyITq9boZkwmFC+bC0f3LrF7n5+crke67cu0uqtTqAgAICkmsnjh9BSdPR36OJdMnDYCfn2m8B4c8Z4IjSWKq1GrodDpoNFqL+2NCqVThd0BgjM8Ti0WYOKY3dDodpsxciWReHpEeK5GIIZWIIZaIzRI6AKBQKCzGVc9+k7B53XSkS5vSmESqX7cSlqzYhnv3n0bZP6VKFdK26XNVIBBg9bIJyJ83O9Zu2IuPn6JOfv35EwwA6NR9DA4dPRflsQG+1y0m2SNKlswDm9dOQ7EieQEAj26bJwoB4Nu3HyhevhW+fYt5opWIiIiI7FOskkg5c2SCRq0xjowRikQY0Kct7j98hhcv35lN3RGKRJBKxZBJpbj34Gm0PqDHlFJpaPPrtx948PB5tM5xdXGG/+/IP9zodHrjPRbIlx1FCueGSqWBRqMxjixInSo5AKBalVLIkd0HV67ehUQqQfmyRXDm3HVjW/17tzFOnfP/ahgNtHz1DgwaNgttWtbBxLG9cPTYBZMRCxXKFUWZUoUwYOgMswQSAGg0GnRs1wAd2zWI1v1GHA0R8V4BQB3uZ9OvV2tkzpQOCqUSzk6OAAyjdMqULgSJWAy5XIZJ05ab/JVdLBahdYva2LX3OD58/IqcOTLh0b8vcOb8deQuVA/BCiU0ag3y5c2OfTsWoEK1Dnj1+j0EQiHkMqnFD60N61WGp6cb1qzfa3IfX75+x7MXby3ejyaKe9XrdSZJQ58MqTF0YCcoFAqoNRoUyJcdHu5JMWvaIMMIKZkM/z5+icXLt5q0M2RAB2RInwplK7dF3jzZsGD2cLTrPNI4uqR963rw8nRDifItkcTZCccOrsCYCYuwYfN+yGQyfP9h/jP9W8lkUqxZPgkODnJMm73KZHRR6HtEr9cja+b0GDeqJ2rVKIvTZ69hz37z0Vzx+fzYuuMIzp6/js9fvkMkEuLLm3NYt3G/yYfxEsXy4djBFRbvq1nrQWajBuvUKo92revhxs0HOHHKNGGj1xueCWKxCG1a1sXoYV0hFAmxaNlWfPny3XjcvftPUbpiGwQG/sGfYENc6cLF5LGDK/Dm3Sd07TnOrE8isRgyqQRabdj3WKsznHvj5kMsWb7N7JyoTJ80wJjwMLYX0nZkSSK1ynIi1dvbA7lyZLY4ojGJsxOCFQqzNiOOoBQKhSFJHwlUanWkye0hAzogezYfKJUqbN84y7hdJBJBIhGbjOKTSMTG5HLS5MXNnnUqlcZklE/DepVNErtVKpYwOX5An7YY0KctACBL7pr4/MXXrH8R7yvU/FnDULdWBSxathXDR8+1eEx42pD3kqUkuyU6fdQj+ypVKIblC8fi9ZuPKF6uJX5b+H9cv16t0aFtfXTpNZ4JJCIiIiKKkVglkfbvXAjvZOZ/FfZO5oFbV3ZGeW7OAnXx7v3n2FzWyMPdFR3bNbQ6pD9P7ixwdUmCC5dume0rViQPdmyegxmz12Dx8q2RfiAIVbZ0YYwe3g0qlRpqjQZaTdg0LADo2a05NGoNxBLDB8CtO44Yk0j582bHoH7tsGTFNpw8fQUioeFDUGjyZczERVi3YjLc3FxMrlm7Rjm8ev0BazfstdgnsViM1ev2RHskkqWaIP16tYZOp0OSkNod1auWRkafNLhz7wk0Gi0USiU0Gq3xQ5lOp4dGo4VAIIBKpTabqlSvdkWkSO6FZSu3w8nJARdPbcC6jfsxdNQcfP/+y9he6Acbf/8A/PCLfFQQYKiT9eXrd1y/+QBA2Pc8KhF/noUL5kLpkgWgUmmQNUsGeHokRa9uLSB3kGHv/pNQKpVQqQ3JwdBzQ+9TLdSYTTOpW7sChg/uhMEjZuP23ce4e/8pBvRpi8YNqmD3vhMoVCAnBvdvj83bDhuTmsdOXETPbs2xYs1Oq/f8NxEKhVi+aCwKFcgJwHzkRaje3Vsia5b0uH3nMarX7YaLl29bPC4+nx8KhdI4gsTJycHiOaHJhuZtBuPZ8zcAgCyZ02PrhplmyZXCBXNhxaJxhvMiSW64u7vi1uWdSJbMHUuWb8P8RRvxO8B0SmlAYBDu3n8S6X2kSuWNE6euRDsOwodzEmcnNGtSHQqFMtKpgmKxGJ8+fzMmwUKTxKGsTTG0tL9o4dxYs3wSPD3cULlWJ9x/8My4z8nJAZ9en4m0vZbNaqFls1pm28dOXIQ5CzaYbS9ZPD8G92+P+w+foVyVdiYjQrt0bIzZ0wYjZ8F6JgmQ0ORSVMnyUKExkSN/HeOz6Mq5Ldi55xiWrNgGtUqDxg2rYtbUQdGecioQCLBwzgi0bVUXcxdswJhwIzGjIhEb/jfcs98knDxtebpxqKf3D0Fs4TkOABl90mDm1EGoXKE4Vq7ZhZHj5mP9yim4duM+Zs9fbzyuXau66NyhEQaPmBXp9GYiIiIiosjEbiRSgbpQqdTQ6/XIns0HF05uwJFjF9Cm4/BIzxGJRJDLpSZD8R0cZNDp9FFOs4qoZPH8WL1sAlKl9IbfT3+sWmt56ppIJMKKReOQLWsGTJ6xEjMjJJyGD+4Mt6QuCFYorSaQAGDuwg2Yu9D8w05ofZ7uvSfg4JGzZvs9Pd2wbeNMvHv/GaPHL7T41/1795+iSOlmUKs1xlouANB/yHS4JXWJdLTAnz/BqFWjLGrVKGu1/1+//bCYfOnfuw3kcpkxSdKgXiWIhELMX7wJk6eHjd5ImyYFWreogw2bD0Q65UIkEmHowA64cfMBbt99jBpVS0On02Ph0s1o3qQGFs0daXZOxKRBnwFTsHbjPuPXjepXQb482UxqeohEhnrwyb09Iy1YLRabhnahgjkxYkgXKFUqyKRSiERCDB/SCQ5yOeYu2IA+A6caj122cAxy58qCYaMsjyJwSeKEsSO64+z5G7hz9zGyZ/OBVqPFqrW78P3HT2TJlA5KlQqbtx3G9l1HjX3csuMIrl6/D6lEgiAEW2z7b+PgIMOGVVNRrUopbNxyAK1b1DHuk8tlqFq5JDq3bwTA8CG6dYfhVqfkxNfzI7SOlkKhsvgelsmkkEokUIa8596+/2QcuSaTSwGEjWAEgCqVSmD9qil49fqDWdIwk09atGpRC1UqloBcJsOxk5cwa966KEdxpEyRDPKQ64TnltQFzk6OCAz6A58MqSM9/9XrDxa3u7m5YM70IZGeF2rfwVNmI6liw9XFGSOGdkGXDo0Q9EeBqTNX4snT1ybHBAUFI0/h+ggOVkKj1UIfLgn15N4hHDxyFoOHG0YTCUUiSCViSKUS/PoVYHa91Km8sXrZBKjVGnToOtrqlOJQWq3WJIEkl8uQI5sPghVKeHi4QigUIGvm9HB2djQ+C2vVKIvKFUugQdO+0Ol0UChUaNKgKjp3aIx1Gw3J++jU9Eri7ITVyyagetXSmD57tbFeWHRIpBI8e/4GfXu2Qt+eraI89tnzN5FOQfzw8St8fX+iYfN+OH7yMgDgxu1HGDeqJ4L+BGP1uj2YOLY3unduirETF0VaM46IiIiIKCqxSiKFJn2kUglWLhkPv5/+6DfIMBqmQ9sGGDKgPdp0HGEcOQIYfsEPCjL94Lxk/mg0ql8lWtdMny4lxozojob1KuPfxy8xaNisKD+sarVatO4wDKuWTcSY4d1QuEBOtO86CkFBwWjaqBoqlCuKVWt3WywiPWZEd3Rs2wASqRgOchlePDqKfQdPYebctXByNB3t8P7DFyiVKri4GEby1KxWBtdvPTTWSvr+/Se27jiC9x++IlPGtNBqtJBIJZBJJXj47wsolSrUr1sJhfLnAABkzZIeADB5XB/jNW7ffYzd+04AMNS6cEvqAoVCifZdR0OltPwBOiLDB24ZMvqkgU6nM47eSJe1MgBDkujR7f3o3H0sDh09B6lUAolEbPUDnEgkglAogFqtwYghnZEtqw/GTVoMnwyp0ahBFazZsBevXn+Ak5MDRo9fCI1Ga6wp4+7uCuj18Pv5G7lzZkan9g1x/NRlY9sODjKMHt7N7JoOcjkAYNyonhg3qqfFfkVc3W7piu1YumI7gLAkUcnyrYzXUSh0UX4fBQIBZDIpFAolfgcEoWiZ5kiVMhluX9llmMKj1pqc7+bmAoVChXq1K4S1IRTA3c0V795/xo7dx6L8vv4tGtSthGpVSmHy9BW4ev2eMYmUNUsGnDu+DhKJGOcu3gQALFiyyew9KRKJzEaGxNfzI3PGtBZHLoWfFvT12w/Ubmg5RoCwEWtSqQRTJ/TDr18BaNyyP9aumGQ8ZtnCMWjZrBZevHoH3+9+CAgMMimsHdm9rlk+ESWL54/02sMGdcKwQZ0s7gsOViBZWssrOb57/xkeqUpGOS04dMqYNQ3qVkKO7BkhFAoglUggkUqwYfMBAIaYHzygA3p1aw5HBzlWrt2NGbNX4/uPXxbbCn2mWKJSqaM16srT0w0Hdi1CqpTe6NF3okmx8sgIhUKIREJIpRL8+aMw/kzTpPLGicOrEKxQhky/leL4oZWQy2XoP2Q6AMO0vsoViqNo4bDVGlu3qAO/n/4xGi3o5OSAzJnTRbvoNWB4Rmm1Wvj6+qFgiSbRvhZgSJDKpBKTEXBKpQpde40zOW7mnDVwTeKE6ZMGoEuHxvD0dEO7LqOw18I0UyIiIiKi6IhTYe0504cgV45MqN2wl7FQsVQqRqqU3tH66+3KNbuw/9AZkyLIi+eZj1bx8nTHzUs78ObtR3ToOtqYULHm+ct3qFi9A8aP6ok+PVvhnwPLMWjYTMyePgRv3n7CqPELLJ536MhZPH7yCjWrl0GZkgUxcNhMfPnii1lTB6Fe7Yomx+Yv2gi//APg6poEADBv1jB8/uKLMpXaGo8ZN2kJ9myfj3kzh5qcm7dIA7x6/QGlSuRHo/pVcPzkZfz8FYBtO48iWch0n1o1ysJp5z/Ge+7Qpj6GDeoIjUYLjUZrdTWf8CRiMSQSMa5cu4daDXpEeezYEd3Rx8JfxS3V7ZizYD3GTlwMV1dnw/2O6olhgzpBpVYjbxFDYekHD59DIBBg89oZ6Dd4Gk6duYrjh1bA0dEBpSq0xqK5I/Hg4XOTIrQzJg9EurQpcPrsNWT0SWPcHhAYhCReYcu3T5vUH7Wql0WugvWi/b0I5eTkgC9vLCcjA3yvm23zTF0KSqUKarUGb95+gnvKEhbOBN48OY7V6/dg4tSw4sqhibqI02NCR8yoNYZC7tGZjhNKIBBAIjHUC7K2Sl9sbN52GK/ffMTlq3dRrkxh4/anz16jZ79JuHTlDhwd5bh3fY/F8yeO6YVUqbwxeMRss1E7cX1+vH77Ednz1YZSpYZOq0Wv7i0wqF97dO4xFidOXYZMJoNEIoajo9xqWyqVGlXrdIWbaxKzQsgz567Fnv0nceLUFaxYPA7Fi+Y1O9/VxRkXT2/CtFkrsXmbYeXFYIUSX7/9QKaQVQBdkjihdYs6WLZqh8nPuFH9Kjh15ip+/voNAFi5ZDzKli5ktb9R0el00RrhWa5sYZQsnh8CoQAyqRRSqQTnzt8AYIjLQX3bYdO2g5gzf73VAtHxwd8/AGMnLkb2bD44d+GmxfdgqJePjpptK1G+pXH66POX7+CRqiQAw8jTHl2bGZPm1SqXAgDsPXAKjRtUMY5clMkkePHyHU6dvWbWdvZsPqhRtbRh5Bv0ximZVSqVgLe3B3bsOobAoD/o0bWZxf7KZVK8fvvJmMDZuNowwi+2AoP+IEX6cpHud3CQoXKF4sifLzsA4Math3jy9DVaNq2BGzcf4MPHr7G+NhERERHZr1glkVxdnDFtUn+0al4bk6evMKk5FDo9JHxR2NBRHBKx2OSD7uWrd83atjRUX6VWo0ffSdh34FS0Rt2Ep9FoMXLcAjx/8RZzZgzFySOrodVq0bT1QLORDaFu332M23cfI1PGtChUICf2HzwNAGjfJhj3Hz5DyfKtUKpEARzdvwwqtRrfv/+Eh3tSODs5Irm3J5at3G7WZpOWA1CkUG4cO7gCuQvVw5evP0xWYvvw8Ss69xhrdt7DW/tMVqebNmsVps1aBQDo3KFRtOoDhbp89S4ePoq66HjRwnlQv24lHP7nHIqXawmVhQ+iW9bPwLPnbzF+ylLIZBJ8//4LADB99hqsXLMbr998wK4tc3Hh8m2TguDfvvnhydNX2LNtHgYMNSSjXr8xTNn55/hF/HP8ovHY7Nl80K51PUyfvRpKldqYRJLLZRZrMUUkFAohk0mgVmssTgd0dJSjX6/W8MmQGmUrt0XQH4XJFBwAKFumMOZMH4JaDXrC97sfZFKp2QfzyKbTCUVCuLu5muxPkcKwap9AKDQ5tmO7Bpg+yXRFuph6++5TrJJo0RH6Pg2NtdD/hiY2U4WsRlixfDHcuvPYWEDa1TUJalYvC3d3V/j7h01biq/nR+j7JvSY0ARvYOAflC9bFHsPnIJWq0X2bD7Rus/v338a41UgEBjv8+Wr93j56j0AQ/Imubcnypctgo8h1xaKRKhbqzzSp0uJ5N6exvaCI6yi5enphv592qBA/hzo2G00AEMR77UrJmHMhEUm02UjqzsV3qUzm5AnVxaL+6rV6YpLV+5YbaPPgKkWk/IliuWDSqVGzgJ1Ih15BBim8/r4pIZSoYJWq7NY9FkgEMDZ2dFs6p5AIIBYLIZUKoZSocKzF2+hVmtw8MhZHDxyFt7ehiTNwGEzsXvvceN5bVrWxYQxvVC4VDP88PsFgUBgnB4X3cRI6CgtlVKFyjU7G7crlWq0aDcEAoEAbVoaRt2F1pLLns0HQwZ0hFKlgk6rgzBkWm2tGmVRtZIhWeXm5gKVSm32/xaRWASZVIKjxy8ak0ijxi3AhKlLMXZEd6jVWowcN99wrFCI21d3oWO30bh5+5FZ34UCIaQyidlIM58MqZEtSwbkzpUFRQrnRqniBQAAO/ccw4ChM5E1S3qsXT4JZ85fj3J1SiIiIiKiqMQqieTu7ormTWoAAEYO7YKRQ7uYHXP+5HqzbR8/fUW2vLVjfD1//4A4D79ft2k/qlQqido1y0Gr1cE9qWuc2gvv/YcvyJQxLfLlzQYA+OfEJZP9mTOmRZ7cWZEhfSoAQIVyxfD7dyBUKhUOHD4LnVaHPLmyRPpX98gSJpPH9cGxk5ewa2/Yh8AaVUujetXS6D1ginGbo4McKxaPQ58BU8ySSEKhEMWL5kXTRtUAAG1a1cGGTQdw+cpd/PIPsFhUVqlS43dAIJ4+ex1SyNbQP19fP/j6+qFH12bIkjkdmrYeaDynSqUS6NOjFbr1Ho9yZQpj/8EzaNa4mrEmSsRpUI+fvELLdkNw8Mg5DOrf3rj94a19FosyA5ZHDtVv2sdYrNbb2wM1qpRGsaJ5kTFDGlSpVAKLlm7B46evLCabsmROD8CQ6Hr/4QukUonJfpckTlEWgu7UviE6tW9ott3RwXRkzJ+gYHz4+DWkD5po14ABDPWhxBIxfoQk8mxJLDZ8mJbJTL8PHz99w94Dp1CvdkU0blDVZN8v/wAMGTHbJPlmi+dHs8bVkSljWgBA4UK5MKBPWxTMnwPDx8yL/g2GIxKJIIqQ7AOALTsOo2rlkjiwy7RoslarxZVrd7F8VVg8hE7bBAyJs6/ffqBb7/HIkS0jhEIhkromwaypg7B1xxHMXbgBcrnM+H6ztgIXACiClTh55iqGhptalytXZqxfOSVeCrfrdLooE0gAkC9vNhzasxgKhQpandasgLeDXIZghRJlSxfGuROmP9PwyZ/LV++iRr3uJvs1Ie+DoKA/JvcT9MeQoPH76W+cNhxTMpkUGo0Gb54eN9k+ZEB7DBkQ9rzRaDTGqbF79p3Enn1h/x/Kns0H1y9sM0nE7doyBxnSp47W9LSnIUXeAwL/QCAQ4EfI9zo0afXpsy8UChVUarXJqpmA4XsQ8XnlncwDWzfMxM+fv3Hm/HV07zsB/xy/iD9/FBjUrx1GDesKkUiEqpVK4utb0xUJAURZDJ+IiIiIKFSskkiv33xE1dpd8PPnbwQE/oEq3ApHTRtXx/RJA1CrQU88fGRYvSd0+XahhQ9lCaVT+4aoXbMcjp24iBLF82PD6ino3mcitu00nxIREwKBAM9fvEXF8sVQuWJxvP/wBY/+fWFyTLGieTF2ZA+EjhkaPrgTREIhPn3xxYHDZ6HX6/Hk6Ss0bT3IrP0j+5ZGeu1ghRJ1apZHrephhbUFAgFEIhHWhavpEiri1LfUqbxx6cwmuLu5GgtX9+o3GQePnEXd2hWwaU3kq77lyZXFuNpSaKJGKBSiXet6mDS2DyZOXYYc2TIiVcpkuHj5Dp48fQ0HuQyXTm9C45YDEBAYBJckzsYPhJYcOHzWbFufAVOg0+mh1WlRpmQh9OvdGh8+fkUyL3dIpRL0GTgVHz5+gUgogkwuxYOQpNnUCf3Qo6uhePnPX7/x4OFz44fWa+e3Ikf2jJH249Ht/QCAGzcfoEL1jsbtybwMyaxuvccbpzCFsjSdLTLrNu3Huk37rR6X2GRSQ00oSbii5UMHdsTXr9+jLIodUXw/P1ySOGHsyO64efsRChXIiRs3H2Lthr3o1b0FTp65ik+fYz4NK2J9o4L5c6Bxw6pYuGSzcXqaNeFHTQ4f0hk9uzY3fj15fF/j6+ZNahiTakmTF492H3V6HQIDg4zFwgEYp9Wq1VFPd4svFy/fjrTPGX3S4PKZzZgyY4WxHllMRExIxadde4/j7PnrcHeL+o8J33z98MvfvPh3ZDZsOYjNa6ejXp2K2HfgVLTPa1ivMhrWq2y2/dHtfWaLBACm0/ZCXbl2D4VLNsXzl++MsZc7V2bMnT4UhQvlwuJlW7EqQg1AuVyGI3uX4smz10wgEREREVG0xLom0rUbhqK3xYrkwe27j401OkKH8f/+HYgffv6Qy2Xw8nQzLmefGOrVqYiZUwZi38FTaNtpJPLmzop9OxZg+aKx0Ov12L7rH6ttuLo4Qy6XmY0YksukuHPvCbp0bIxG9atY/OCwcctBbNxyEMWL5sXxQytRsXpHk2XKAUCl1lhcjSmyldkAQzHgZat2YP6iTcZtHdrWR4c29VGqYhvjtiRJnHDz0nazqW8fPn7FqTNXcfjoedy8/QgPb+0zfvi4cPEWylRqC51eZzYtJ3Q624Spy+DoIDNO9Uni7Igp4/tCIhFj7Mju+PzlOz5//oZzF27C97sfmrQagHat6xmXPnd1dcbPn7+N7YrFIshk0kinGQLAkWMXjK/HjuyBh4+e48Ll26hRtTTef/iCYkXyomuvvRbP8/BIinGTlmDMiG7IHW4aUIduoyEUCqDXw2T6XvjpbH4/f5n9LJIlcwdgSNxFnNZmaTpbeL9+B0a5stff6NDRc/BKU9pkW8N6lfHg0fMYJ8Hi8/kxZUI/eHq4oVX7YThzbC0AYPiYeahetTTmzhiK1h2HxahvAFC2cluTr9OkSYGeXZtj1RrLq0Fas3DJZqzbuB8atQY6vQ59erRC21Z1jCNWREIhHBzlMaqHJRREnpS3NIoqoU0Y3QuOjnK4JHE2biteNC+u33wY6X2mT5cSLi7OUKk0cAgZAeTl6W4yFc7DPSkAIF2aFHB2ClvoQCQUhixaIMX9h8+sfi+7dW6KoQM7RnnMoOGzsHxV9FcxO3j4LO49eIoZkwfgzNlr8P8dGK3ztu08apzOLBKJ8OuLYVW9SjU64Z8Dy+GTvRoCAoPQsV1D9O/d2iyBFCo0oZg3T1Z0bt8ILZvVxOs3H1G7YS/4pE+FN28/mYxsnTqxP5ydHSNdiZKIiIiIKKI4FdZOlzYldmyeg+fP36Ja3a4Wp+FsWjMVeXJnRd3GvfH4yau4XC5WunVugqkT+uH0uevo0HU0dDod7tx7jNqNeuLQniVYPG8U3n/4YrE+E2BIwOzdsQAvX73DoOGzMGaC6TSWT5+/4cdPf0gkYqRNkwKbth2KVr+EQqHJL/NRTWeLjEajRZuWdYy1O4Cw4tm3rph+8AkM+mPxL9oduhpqs6RNk8Jku99PfxQulAsLZg9H5ZqdTZJeodPZnjw1/Xn6/w5Ey/ZDERAQhAePniE4XMH00BWuAJisqDZqWFeMGtbV+PXL1++Rr4j5FLCIundpiry5s6JWg56oXtVQnHbKjJU4sm8pTpy6jF17TaepXLh0y6T2TnjPnr/B1vUz8Pb9ZwwcGlY4PPx0tohJPwDw8jIkkZYuGGOx3cimswHA/EUbMWr8wqhv8i9QrkxhFCqQEwqFyuIUK1dXZ/hkSG2xmLAAhqLfAYF/LK6CGB/Pjzq1yqNtq7oYM2ERHoeLx6CgYIyduBgikdBqEWrA8MF9UL92UCpVJjXIQuXMngkA0LRxNWMBbJPzhULI5DIcPHzWbEWxJM5OyJbVBz9/+iNQq4UQhhUNAUAcMnUJMLyfQ+tLRYdQJISzs5NJojJNmuQAAKlMGu12Qjk5OaB7l2a4EsmzMCZaNK2JOrXKY/b8dZg+ezUAoH7dStiwago2bjmAHn3NR0oCwMihXdGscXVoNIbppYFBfzB0UEcMHRSW7AkdCXdw92LoETZaSSgQGlaZk0rgk72qyRQ4iUQMqUSCgX3bImeOTLhy7R4USlWktcRcXZzx4eVpBAcrzPZFRSAQYOjIOTi8dwm2rJ+B+k37Riv+mjSsikb1zUci3brzLz5/+Y76dStiw+YDaNa4mtXRs4MHdMCY4d3w6fM3DBs9D2vW70HRwnkwd+ZQlCieH117jYder0eHtg3Qq1tzDBs115jYJyIiIiKyJtZJJFcXZ2zfOAtJnB2xeMXWSOu4LF+9E5vXTsc/+5ejQbO+uHXnX7NjUqfyRplShVChXFGTwrRRyZA+Fd69/xLpX5uzZfXB1In9UKl8MezedwJdeo4z6eP9B8/Qqv0w7NuxACOGdDFZrSyTT1rUrF4GrZrXgrubK5ydHLB3/ymTItE5c2RCiyY1sGLNLgQF/YFCoUJgYBAeP3kFuVyGVCmTGUfoRCQSCbFtw0z0GjDFOBolsulsR/dbng4V+kEp4ve9T4+W6NOzldmUG5FIBKk0Zj9uf/8ApEyRDHOmD0ajFtEr/HzqzFWTr729PaBWaTB6wiJMmbESSpUKGrUGo4Z1Q6vmtVCqYht8/+4HgVAIsUhkrLsTlQL5smPC6F5Ys34Pzl24YUwiXbh0C1u2H8bieaOg0WqjPZ1ErdZAIpGgU7uG2Lr9iMVitpbsP3jaZJU4qVSC2dMGo3GDqpBIxJi3aKPZdDaBQIDf367h0xffaF0jtmpULY05M4YAAEaNW2iWVIuusqULoWfX5ghWKKG3ML3I1dUZ7m6uyJAuldk+gUAAsUSEN28/mSWR4uP5UbhgLqxcPB4nTl/B3IUb4BRuVAoAbNlumGIYWlj78pnNkd6nWCzC0IEdEaxQQK0y74sk5L3TpWNji98Hocgw5e7Z8zdmSaTUqb2xb4fllSAj1tSas8C8FlRkHOQyFCqQ02JdLrekLtFvx0GGjm0bYkCfNvDyckefcPXULBk+uDP2HjhllkQOVapEAcyfNQw7dh/DuElLjNv37j+J9eWKom2ruvjw8SumzFhpdm7nHmMtLjAQXpeOjTF72mDkKdIgytF86dOlRL9ebVCyeD5ky2qIgaEDO+L8xZt4+eo98uXJinRpU0aZvI/uyDChUIjB/dujRLF8qNu4N6bMWInRw7th99Z5aN9lpNXaUjt2HzMbiZQ/bzZ8/uKL+Ys3YcLoXpBIJMifNztad4h66ujCJZvx/v1n7Nl/0pjAunj5Ntp3HY01yyZCqVTB76c/BvRpi9nz12Hx8q3RukciIiIiIiCWSaQkzk7Ys20+smZJj47dx5gUG43oxKkraNp6ELZvnIUDuxehQdO+uHbjAYoWzo2unZqgcMHcSJ8uJRQKJW7efmRWt0en0xsLjYbXr1drVK5YAnUb9cLzl+/M9ndoWx8VyhbB9NmrMWnacot9O3/xJvoMnIKDEWrvlC5VEONG9cC+g6fRst1Q3L3/BF5e7qhdoxzKlC6EyhWLI0O6VLh+8wHmLtyAuTOHQqPVIFkyD3Rq3xCfP/ti5dLxqFi9I/59/NLYbuh0shWLx6FYkbwoX6awcSpdZNPZIqsLMnJoFwzo0xY6nc5kRFNoTaSfny+bbXv2/E20Cr6Gunr9Pg4fPYea1cuiZIn8uHQ56tWeihbOjUYNqiJFck+kTZMCGX3SwCWJM9p1HmmyAlSvbi3QoW19TJ25Cp6eSbF47kiMm7wkWjU5CubPgT3b5+PJs9cYNtp8CsaAoTOQOVM6rF85GRvKFcW0WauitTT5mImLcP7Eeowf3RM16/ewenxElSoUw9QJ/ZAkiROq1+2GvdvnWzwuU8gqc+/f23Z6p4ODHKlSegOAWXIlJsZPXorxkyOvy3X5zGbcf/gU3XpPiHab8fH8AAzJv6vX76FjyGi6yIQWRG/eZjCehRQzzpI5PbZumGlMWiqVKrinLBFpG7VrlMOW9TNQumIbi6PSLBGFrN719NkbeKcrYxjNFfJeHT+6J/r0aAm3FGHXFItFEIlEWDR3ZLTaL1a2RbSOsyS0nlfrFrUxa+ogeHq64diJi5g5dy2u3XiAyeP6WFz5MWuWDBgxpDOKFM6N+k36mO0vW7owtm2ciXMXb6Jb7/Fm+/sMnIrUqbwxfHBnfP7si7Ub98X6Hqzx/f4TjRtWgVarw6atB3H46HmcPncNf0Km5+bLkxUfP301K+gNAM7Ojrh0epPZ9vBCvz+5c2VGn56tUCBfdjx99hrp0qbEjDlr4Onphu6dm+LSmU0YNW4hdu45ZrEdoVCItGmSoUfXZsicMR0yh4wsGzm0K16+eo+1G/aifp2KmDdzKFau2YUvX79H2S+FQmlxtNI/xy9i557jaNe6HoKCgjFg6AysXLMryraIiIiIiCKKcRLJ09MNe7bNQ55cWdCt9wSTD4BOTg4oVCAnANMizmfOXUerDsOwdf1MrFs5BfmKNsTLV+9RsVwxnD53DcNHzzX+cv/sgWmB4m++P5DMyx1NGlbF3XuGIfdubi6oU7M8vvn6GRNIET/vDBkxGytW7cSLV+YJpvA2bTWffrZ52yHcvvsv7t1/atzWqH5lzJg8EE+fvcaGTfuxY/cxfPj4FTOmDESdmuXRrPUgVK9aGlMn9IO/fwC+fPluNv0mfchojWJF8mLMhEXGBJKlD2uhHB3lFrevXLMLi5dvw48fv0z+Wh7ZSCSpVAJnJ8dIr+MQsmJYxJXgxk5aAr0eePo0bHSFg1xmUjQ41O+AIHTp0AgPHj3HlWv3sHTFdjx5+hpv3n5EzhyZULxoXjRrVB1Fi+TBxi0HMH32apQsnh9OTg44un8ZTpy+gjETFpmvIBey3HrRwrlxeO9SfPn6Hc1aDzJOlwv//QsKCkbjFv2xYfVUtGtdDyfPXMXHT6fN7jXifd67/xRr1u81SWSFrspk6V4N7cjQqH4VdGrfCPnyZMWmrYcwYsw8szooAoEAk8f1gZubC4oXywelUoUbtx5abPO/RiCIOn4jiq/nh1Kpwt37T1C3ce+wvoSUro/YHwEEePn6PZ6/eGusGaNUqfHP8YsIDPwT7fu01HZUHMO9p/5EqCsmgMCsULhGo420BloMLmsi4oqCocqVKQQAKF+2CA4cPoMZc9aY1Nn5HRAEmUyKvj1bGUdUCgQCtG1dFwCwdbvpc1omk6JHl2YYNcyQ+Fi6Yjvq1qqA5N6eSJHcE6lSeSN1Sm+kTZvCuLri3JlD8eOnPw4cOhOjewpN/Fn7ngQFBaNB0764e/+pyeqA8SV0NcCBfdshKCgYw0bNxdKV243PliEjZuPjx68YO7IH1iyfiEH92qFrr/HGqWP16lTEgtnDjaPGPD2S4vLVuzhw+AzKli6Epq0H4dyFG/D0SAovTzc8e/4GrZrXwg+/X1i4ZDN+BwRZ7WP6dClRvGg+lClVELVrloejgxy79h7H2ImLo50MJSIiIiIKL8ZJJL1OB5VSjb6Dppn8tXPezGHo2K4BAMNS3BGndBw/eRnd+0zAk2evoVSqoFSqkD1/bbMPV1KJxGRK05bth1GrRjksWzgWEklYd1+8eoeuvcL+0h26z8PDtJhxZIWNwxMIhZBKxXj//gt++QdApVKbJJAAYMPmA7j/8JnJaJyNa6aiXu2KGDZqLo4ev4jjp65Aq9WhccOq6N15pFnyoVSJ/ACAEWPmYeHSLcbtIgtTuDq2a4h2revC3c3VuOx3qHRpUxq+hwqlSWFZAJDJZRDAMF3IkhTJvRAcrDCuOJQmdXIsWzjGmOB6GWE01NNnr9G87WAAwKxpg5A+bUpkzpQOR/4xXyL68ZNXSJ+1ilnNmL49W2HSOMOohXMXbqJGve7G+kQXL99GyQqt0blDI4wZ0Q0bVk1BkdLNTD5Mi8WGaW7XbjxAz36TcOXaPZPRRSKR0CRmfvj5o1aDnqhSqQSOnbhk3D50YEdUqVgC+fNlx8EjZ83633/IdABAmVKF0K51XRQtnAdardak+Heo6ZMHoH3renBwkOPYyUsoXakN7j94ZtwvkYqNtVv0ej2kMgnKly2CW7f/Ra9+k62OJoir3ftOYM/+k/j0+kysl0GPDrFYZPK+tCa+nh+WyOWGOkAREyd37z8xq7P19t0nNG4ZvSmaACAK+VlKY3CvoUlIS6QyCYRCIWQyqfF+qlQqgcoVS6Bs6UL49Nl0uqOLs1O0rwsY3v+L5o1ElkzpAQABERIO8xZuRJ7cWTF52nLcvvvY7Pztu46iWePqxvdtKK1Wi+27/sGuvSdMtjvIZRgysAOkUgmyZ/PB7q1z8e79Fzx/+RavXn/A7Tv/Yt/B0/j06Rs+ff6GtGlS4MCuRVi9dALKvWpvtqJlVGRSw89ZbGGEakSho9YsEYpESJXSG/eu74n0GEujYEOVKVUQAHD/4TO07TjC4h8s5i/ehPMXb2H29MG4ceuhSe2hK9fuYu/+kzh38SYuXLptfJ96uLti9rTBkErFqFShGGZMHogPH7+iccsBKFemMJYvHIte3Vtg3cb9GB5uNGb+vNnRr3drOIaMQkyfPiWShMTN/YfPsGDxJmzedihaIzOJiIiIiCIT4yTSDz9/VK3T1axWxIIlmyCXS3HtxgMcOHTaYo2THbtNh/NHTCABgFgiNv4F33i92l2s9stBbjina8cm6Nox+lO2wmveZjAOHT1ncV9QULDZdK4+A6Zi194T2H/QMNJFq9Wi/5DpxmRERDPnrkVgULBJAgmAyf2Gev/hM/LlyYanz16bfGBzcnLA3Wu7oFCqoFRE/td1Sx+MhCIhHOQyzFu0EZOnrwi5zheIRCK8ffcJ02atMvvwHp5arUXePNlw8PBZLItkxSJLRYfnL94EnU6H0+euW/ywqNPpsHzVDhw8fBZJnB3NRmPIZTLIZYYP5JZW0nN0kBt//qH0er1JAgkAHj99hUb1K2P7rqOYONXyFEcAePTvc1StXBLv3n3G/MWbEBhkPlpl0dItkEmlWL56h8WCzw5yOeQOYUmEQcNmYdCwWZFe0xZCp62cvXDDZteQyWRwcoz+dLn4fH5EJA1JLoQmk+KTPKRQtWMMpgY6WHhfh1qzbg/+OX7R5D6fPX+DdSsn4979p5g2a7XJ8a6uSWLUX//fgfBO5gHvZB745/hFs/eN/+9ANGzWL9Lz37z9hPzFGkEulxmn5QGASqW2+LP55R+AiVOXwUEuw+Wrd3HvwVOLz/dQHz5+Rd9BU6FQqGKUQALC/mBg6bkZE3KZ1Gph7agSgWMnLoZSocLEacvNEv3h3bn3GBWqdTAbefb16w/0HTTN7HhxyP05OMhRoWxRXLl2D/0GT4NarcGxE5eQt0gDdOvcFLfvmtYXvHPvMVKm8IKjowPevP2Ik6ev4OadR7hx8yE+27gGGxERERHZD4GzZ2HLc3XsiFAohFQqMa4I9DeQSiVwc3PB16//rWXg6e+QxNkJu7bOxcix86NdKJwSn0AgiHT6ZEyJRKJoF4YmIiIiIiKKDiaRiIiIiIiIiIjIKqH1Q4iIiIiIiIiIyN4xiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFYxiURERERERERERFaJE7sDFHt6gRB697TQJs8GXbLM0EudALEUEEkArRrQqCBQBUH47TlEX55A4PcOAr0usbtNFGOMdbInjHeyJ4x3sieMd7IXjPX/b0wi/YfoBSJoM5aANn0haFPkhM47CyCRG3Zq1SFHCQCBANDrAegNm0QSw3/VCgi/PoPo8yOI3tyE6OVlCPTahL4NIqsY62RPGO9kTxjvZE8Y72QvGOv2ReDsWVif2J2gqOmcPKDJVxfqAo2gd/Y0vBGFYsObMKb0ekCnAUQSCAK/Q3JrF8T39kMY9CP+O04UQ4x1sieMd7InjHeyJ4x3sheMdfvEJNJfTJs6H1SFmkCbtbxhg1AUvxfQ64GQYYOiJ6chvbUDog/34vcaRNHAWCd7wngne8J4J3vCeCd7wVi3b0wi/YX0chcoKg+ENld1QKsBRAkw6zDkOqKHRyE/MRsCxW/bX5PsHmOd7AnjnewJ453sCeOd7AVjnQAmkf46mkyloKg5GpC7xH9GNzp0WgiC/SE7MgniFxcT/vpkNxjrZE8Y72RPGO9kTxjvZC8Y6xSKSaS/hElWV6dNnDdmqJDrix8egezEbAgUAYnXF/q/w1gne8J4J3vCeCd7wngne8FYp4iYRPoL6NzTIbjFEuid3BP3TRmRTgtBkB8ctnSH0O9dYveG/g8w1smeMN7JnjDeyZ4w3sleMNbJEiaREpnWOwuCmy8GpE4JM6c0prQaQBUEh609Ifr6LLF7Q/9hjHWyJ4x3sieMd7InjHeyF4x1igyTSIlI650Vwa2WA2KpYSnEv5VOA2hUcNjUhW9QihXGOtkTxjvZE8Y72RPGO9kLxjpFRZjYHbBXOvd0hszu3/7GBAz9E0sR3HwxdO5pE7s39B/DWCd7wngne8J4J3vCeCd7wVgna5hESgR6uQuCWywBpI5//xszlFAMSJ0Q3GIp9PIkid0b+o9grJM9YbyTPWG8kz1hvJO9YKxTdDCJlAiUlQcYipP9jXNLoyISQ+/kDmWlAYndE/qPYKyTPWG8kz1hvJM9YbyTvWCsU3QwiZTANJlKQZOrxt9V3T4mhCJocteEJlOpxO4J/eUY62RPGO9kTxjvZE8Y72QvGOsUXUwiJSC93AWKmqMBnTaxuxI3Oi2UNUZxuCBFirFO9oTxTvaE8U72hPFO9oKxTjHBJFICUlQeCMhd/rvZ3VBCEfQOrlBWHpjYPaG/FGOd7AnjnewJ453sCeOd7AVjnWKCSaQEok2dF9pc1f/7b8xQQhE0uWpAmzpvYveE/jKMdbInjHeyJ4x3sieMd7IXjHWKKSaREoiqYBNAq0nsbsQvrQaqgo0Tuxf0l2Gskz1hvJM9YbyTPWG8k71grFNMMYmUAHROHtBmq/Dfq3JvjUgMbbYK0Dl5JHZP6C/BWCd7wngne8J4J3vCeCd7wVin2GASKQFo8tZN7C7YkACavHUSuxP0l2Cskz1hvJM9YbyTPWG8k71grFNsMIlkY3qBCOqCjQDB/+m3WiCEukAj6AX/J3NoKdYY62RPGO9kTxjvZE8Y72QvGOsUW/+nEfP30GYsAb2zJyAQJHZXbEMggD6JF7QZSyR2TyiRMdbJnjDeyZ4w3smeMN7JXjDWKbaYRLIxbfpCgFad2N2IlEwEeMjj2IhWDW26QvHSH/rvYqyTPWG8kz1hvJM9YbyTvWCsU2wxiWRj2hQ5AWH8FCpbVAE4Y6HIvLMEmFcO+NgFUPcDvnQFJpcEROGSyuXTAG86AQ/bALk9w7Z3yAUUSxHHjgnF0KbMEcdG6L8uPmK9Zz7gaXtA1Rf43QvYVhPwcgjbXywFoB9g/u9nj7BjGOuUEBLi2R5eqVSAph8wMcIf0xjvlBAS4vkeykEMXG8BjC1uvo/xTgkhIZ7v3o6G98CnLsCfPsDzDsCEEoAk3CczxjvZGn93p9hiEsmG9AIhdN5Z4mWI4OBChjepJdtqAt3zAodeAd1OApc+ASOKArPLhh0zpyxw4wvgGwyMD/kQIgBQPT1w+HUcOycQQOedFXr8nw6FJKviI9aHFDb8svXyF9D7DLD8PtAwM/BPA0AY0mzR5IBSA7Q6avqvy8mwdhjrZGsJ9WwP5SgG1lYFnv8CJl4z3cd4J1tLqOc7YPgAvbkGUDi55XYY72RrCfF8FwuB4w2B4imA2beAvmeAix+B0cWAFZXDjmO8ky3xd3eKi/+ztfz+Lnr3tIAkbmPwxEJgYXmgXU7A94/5/poZgJo+QJ19wMFXhm2rHwIXmgKdcgODzwNqHZDNHRh2EUjvAvTNbziuTsawc+JMIofePS0Efm/jqUH6L4lrrHs6AGOLAXNuAQPPhW1XaoGRRQ2/aF36BBRPCdzxBTY/jrwtxjrZWkI828ObUQbwcQXKbAdUWtN9jHeytYR6vns7AnvqAGmSRN4W451sLSGe7+XTAHm8gEq7gFPvDNtWPjB86G6ZDehygr+7k+3xd3eKC45EsiFt8mxxbqNESkOSqNIu4NEP8/2/lECv0+Zvsnu+gJPE8A8w/A9NrQU0OsNrAGiTA9jwb5y7aKRNkT3+GqP/lLjGurMEmHwNGHPZdPvdb4b/ejka/lssBXD+Q9RtMdbJ1hLi2R6qQhrDSNOl9wy/jEXEeCdbS6jne9c8gF4PFNsaeVuMd7K1hHi+u8kM/w2MUIpGqQV0esM/gPFOtsXf3SkumESyIV2yzHEuVvbED8i13vKHB8CwffFd8+1FkwPvfhuSTADwMRAomgIo5G14XTg5cP+74X9Y8UKrNtwv2aW4xvqb38CU60BQhCaKhsyDvucLpHAC0rkAJVMCbzsBwX0Mc6f75jedDsFYJ1tLiGc7YPgFbU1VwF8JHH9r+KtcehfTYxjvZGsJ8XwHgM1PgNLbgU+BkbfFeCdbS4jn+4WPQIAKmFUGyJjUMGW5YWagdXZg7wtAG5JEYryTLfF3d4oLTmezIb3UKc5tfLMyzcGScmmAQslNM8ML7hhqJKm1QMujQI+8wJDzhvoDal2cuwkA0Esd46ch+s+Jj1iPyF1uKKh3+h3w2h9oFPLsd5MDS+4ZfgFrmBmYVx5I7gQMv2jYz1gnW0uoZ/v4EoZfvgBgXVVALjasVLLkHtD/rOEvdox3srWEeL4Dhpoa1jDeydYS4vn+OcgwSulEQ+BFh7DtW54AnY6Hfc14J1vi7+4UF0wi2ZJYCiRwES9HMbC8EvA+AJh9M2z7nFvAwZeAQmvoUbnUQPtcwKQSwLUvQI29hjd27AlC7pfskg1ifUF5wEUKDDpv+PptADDyIjD3NhCsMWxbeg842gDolQ+Yeh34rWKsUwJIgGd7amdDXAeqgHoHDHUz5GJgeBFgTDFDDI+4yHinBJAAz/foYryTzSXA810kMPyRwFFiiGffYKBSWqBeRuBiTsPvNgDjnWyMv7tTHHA6my2JJPGyukNMLKtkKMDa5h/gj8Z03/NfhuRSn/zAhseGN+bIS4a/dLeK6zRRgQAQ8c1pt+I51jvmAlpmB8ZfAe6EzK2+8cUwbDY4XFzrAax6ADhLgZKpwrYz1smmEuDZXiMDIBUBc26HFV5VaICxl4H7vob3SCjGO9lUAjzfY4LxTjaVAM/3zrkNSaPKu4E6+4GOx4FMa4Az7w0FuX1cw45lvJPN8Hd3igMmkWxJqzZUiUwgffIDrXMY/jp99r3lY5JIDcMHNTpAIgIW3QWufgayu8fx4no9oI1Tipj+y+Ix1oskNywXevgVMOma9eODQv7HlDLCqFzGOtlMAjzbPRwM/z1pYSGRJ35AMsew4pMA451sKBGf75FhvJPNJMDzvWp6wx8Dwv+urtYBM24CIiFQOpXp8Yx3sgn+7k5xwCSSLWlUMORbba9mBsNc0k2PgZk3Iz+uS27DMqIOIRMZFRpDdthREtce6EPul+xSPMV6ehdgf13glT/Q/IjpvmmlgUGFzM/J72X4b8QaBIx1spkEeLZ/DCkubOkqyZ0AP4Xhl6xQjHeymQR4vscU451sJgGe7wIYRppG5BgSz+IIn84Y72QT/N2d4oBJJBsSqIIS5Drl0gA7ahkytR2PR36cSACUSgWc+xBWSd9RAjhJDHU34kqgikUVcPq/EB+xnsIJONnIsFpDrX3mc5/TJgGGFga8HMK2pUkC9C0A/FYCZ8MtH8pYJ1tKiGf76XeGJFHLCCvw5vIEiqcAjr0J28Z4J1tKiOd7TDDeyZYS4vn+yh/I4WFYgSqUgxgYUtjw+tbXsO2Md7IV/u5OccHC2jYk/PbcMN/UhtzlwM5ahr9a7HwGNM5iuv/E27Asb5OswI5nhtfPfgJKDTCuuGHY7MhLceyISGK4X7JL8RHrm2sYlrpdeAcokdLwL9R9X8Oc6nqZgBstgQMvDatUNcxsWPGh03HT/3Ex1smWEuLZ/iHQMKp0eBHA0wE4+c5QbLtnPsNf5cZfCTuW8U62lBDP9wffo98W451sKSGe76sfAt3yApebGf4AHKwBCiQzTGPe+gS46xt2LOOdbIW/u1NcMIlkQ6IvT2x+jTKpDR8wAGB+efP95XaEJZFKpAT6nTG8DlIDvc8AU0sBFz4apsHFlehzPDRC/0lxjXU3OVA+jeF17/xA7wj7x10xfGguuwOYVsqwfKhSC1z5ZKghcP6D6fGMdbKlhHi2A4b6do9/AF3zGOIeAG5+BYZeAJ7+DDuO8U62lBDP95gkkRjvZEsJ8Xx/9MPw+8y44kCxFEASCfD1j6H+y9AIKxYy3slW+Ls7xYXA2bNwwlV+tjN6gRBBA88CEnlid8X21Ao4zSoLQQLVgKK/C2Od7AnjnewJ453sCeOd7AVjneKCNZFsSKDXQfj1WYKu0JYo9HoIvz7lG9OOMdbJnjDeyZ4w3smeMN7JXjDWKS6YRLIx0edHgE6T2N2wLZ0Gok//JnYvKJEx1smeMN7JnjDeyZ4w3sleMNYptphEsjHRm5s2L9CX6EQSiN7eTOxeUCJjrJM9YbyTPWG8kz1hvJO9YKxTbDGJZGOil5chCPz+/ztUUK+HIMAXopeXE7snlMgY62RPGO9kTxjvZE8Y72QvGOsUW0wi2ZhAr4Xk1i5Ar0vsrtiGXgfJ7V0Q6LWJ3RNKZIx1sieMd7InjHeyJ4x3sheMdYotJpESgPje/sTugg3pIb53ILE7QX8JxjrZE8Y72RPGO9kTxjvZC8Y6xQaTSAlAGPQDoienAe3/WeEyrQaiJ6chDPqR2D2hvwRjnewJ453sCeOd7AnjnewFY51ig0mkBCK9tQMQiRO7G/FLJIb01s7E7gX9ZRjrZE8Y72RPGO9kTxjvZC8Y6xRTTCIlENGHexA9PAro/k/mZOq0ED88AtGHe4ndE/rLMNbJnjDeyZ4w3smeMN7JXjDWKaaYREpA8hOzAcXv//4bVKeFINgfshOzE7sn9JdirJM9YbyTPWG8kz1hvJO9YKxTTDCJlIAEit+QH54ICEWJ3ZW4EYogOzIJAkVAYveE/lKMdbInjHeyJ4x3sieMd7IXjHWKCSaREpj4xUWIHx7572Z5dVqIHxyG+MXFxO4J/eUY62RPGO9kTxjvZE8Y72QvGOsUXUwiJQLZiTkQBPn996rgazUQBPlBdnJOYveE/iMY62RPGO9kTxjvZE8Y72QvGOsUHUwiJQKB4jcctvQAVEGA7j/yBtVpAFUQHLZ05/BAijbGOtkTxjvZE8Y72RPGO9kLxjpFB5NIiUTo9xYOW3sCGtXf/wbVaQCNCg5be0Lo9y6xe0P/MYx1sieMd7InjHeyJ4x3sheMdbKGSaREJPr6DA6bugDKoL93yKBWAyiD4LCxM0RfnyV2b+g/irFO9oTxTvaE8U72hPFO9oKxTlEROHsW1id2J+ydzj0tglsshd7J/e+qiK/TQhDkB4ct3ZnZpXjBWCd7wngne8J4J3vCeCd7wVgnS5hE+kvo5S5QVh4ATa4ahor4ifkmDbm++MFhyE7O4dxSileMdbInjHeyJ4x3sieMd7IXjHWKiEmkv4wmUykoao4G5C6J8wbVaSEI9ofsyCQuj0g2xVgne8J4J3vCeCd7wngne8FYp1BMIv2FTLK9Wg0gEtv+oiHXET88AtmJ2czqUoJgrJM9YbyTPWG8kz1hvJO9YKwTwCTSX02bOi9UBZtAm62CYYNACAgE8XcBvR7Q6wAAoienIL21E6IP9+KvfaJoYqyTPWG8kz1hvJM9YbyTvWCs2zcmkf4DdE4e0OStC3XBRtA7ewJaNSAUx+6NqtcblkIUSSAI8IXk9i6I7x2AMOhH/HecKIYY62RPGO9kTxjvZE8Y72QvGOv2iUmk/xC9QARtxhLQpisEbcoc0HlnBSRyw06tOuQogeFNq9cDCPnRiiSG/6oVEH59CtGnfyF6exOil5ch0GsT+jaIrGKskz1hvJM9YbyTPWG8k71grNsXJpH+w/QCIfRuaaBNkR26ZJmhlzoCYikgkgJaFaBRQaD6A+G35xB9fgzBz/cQhAwLJPovYayTPWG8kz1hvJM9YbyTvWCs/39jEomIiIiIiIiIiKwSJnYHiIiIiIiIiIjo78ckEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERERERERERWcUkEhERUQLKlycbZk8fjPWrpmBAnzZwdJQb9z17cBgBvtdN/nVo28C4P1fOzDh2cAV831/AlzfnsHTBaMhk0kivtXX9TCxbOMam90NERERE9kOc2B0gIiKyFyWL58fe7fOxZ/9JvHr9Ht27NEO1KqVQpVYXeHm5I0VyLzRq0R/fvvkZz3n/4TMAwNPTDft2LMC7958xZMRspE+fCv16tYafnz9Gjltgdq1G9augVo2y2LztUILdHxERERH9f2MSiYiIKIHMnjYYcxZswLRZqwAAx05cwonDq5A7V2Z4J/PA74BAHDtxyeK53Ts3xafP31CtTleoVGoAQIrkXqhXp6JZEsnTIylmThkI/9+Btr0hIiIiIrIrTCIRERElAKlUgkXLtuDAoTPGbZ+/fAcAyKRS5MuTDXfuPon0/NXr9mDthr3GBBIA+P30h1QqMTt29vQhePLsNd69/xyPd0BERERE9o41kYiIiBKASqXGpq2H8DsgCAAgEAjQt2crvHn7CXfvP0G+vNmQJnVyPLy1D9/enceZY2tRsXwx4/mfPn/Dh49fTdqsVL4Yrl6/Z7KtTs1yqFKpBLr3mQC9Xm/7GyMiIiIiu8EkEhERUQJr37oebl3egfp1KqJRi/7QaLQomD8n/H8HYMzERWjedgh8ff2wY9NsZMmUzmIbTRtVQ9YsGbBwyRbjNrekLpgzYyhGj1+IN28/JdTtEBEREZGdYBKJiIgogT1++grnL96Cs7MjOrYzrL7WrM0g1KjbHXv2ncSpM1fRvO0QfPrsi3at65md7+XljmkT+2PnnmO4fvOBcfuMKQPx+MkrrFq7O6FuhYiIiIjsCGsiERERJbCr1+/j6vX7+Of4RezcMgd79p3A1ev3TY7RarW4efshcufKYnb+soVjoFSpMHDoTOO2qpVLoka10ihWpoXN+09ERERE9olJJCIiogQgFAqRNk1yk2lmx05egkajQf582SGVSnH+4k2Tc9ySukAul5psGzaoE8qXKYJaDXrg56/fxu3161SESxJn/HvngNm1Wzarhep1u+Hi5dvxfFdEREREZE84nY2IiCgBpEmdHA9u7kPB/DmM2zJmSAOxWAytVoeDuxcho08a4760aVKgRLF8uHHzoXFb4wZVMXxwJ4wevxCXr941aX/StOUoUb6lyb8j/5zHkX/Oo0T5lrhz77HN75GIiIiI/r8JnD0Lc+kWIiKiBLB57XQUyJ8DYycuwg8/f4wZ0R0CAVChWgcc2LUIXp5uWLJiO6RSCXp3bwFnJ0eUrNAKHz99Q0afNLh8ZjMePnqOQcNnmbT78N/nUKs1ZtdbtnAMAKBb7wkJcn9ERERE9P+NSSQiIqIEksTZCRPG9ELdWuUhFArxz4mLGDVuAb7/+AVPTzfMnjYYlSsWh1qlwYnTVzBq3AJ8+fodANCza3NMm9TfYrs5C9TFu/efzbYziURERERE8YlJJCIiIiIiIiIisoo1kYiIiIiIiIiIyComkYiIiIiIiIiIyComkYiIiIiIiIiIyComkYiIiIiIiIiIyComkYiIiIiIiIiIyCpxYneAiIjIVgKq1kjsLlAkkhw7kthdICIiIqIY4kgkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiIiIiKyikkkIiIiIiKiWHJ3c8Wa5RPx8dVp/Ph4CUf2LUXaNCmM+7t2aoJ71/fg69vzuHJ2M2pWK2Nyfoli+XBk31J8e3ce/945gL49W0V6rbUrJuHymc0QCvkxjogSB58+REREREREsbR+1RSULJ4f02etxugJC5EzeyZsXDMNAFCpQjGMHt4N02atQoPmfXHj1kNsWjsNuXJmBgDkzZMVB3cvxrPnb1GnUS8sWLIJo4d3w+jh3cyuU7F8MTSqXwWDR8yCTqdL0HskIgolTuwOEBERERER/ReVK1MYhQrmRNHSzfHu/WcAQGDgHyyeNwrp0qZEk4bVsH3XUWzdcQQAcOnyHVSpVALVq5bGw0fPMaBPW9y99wT9BhuSTlev34eXpzt6dm2OqTNXQqPRAgBkMilmTx+MHbuP4dKVO4lzs0RE4EgkIiIiIiKiWLl951+Uq9LemEACAD8/fwCAVCKGh0dSCAWmH7nEYjGUCiUAIH/e7Dh97prJ/qfPXsPJyQGeHm7GbUMHdoR3Mg+MGrfAVrdCRBQtTCIRERERERHFwu+AIDx99tpkW+WKxeHr64cXr97jzNnraNywKkoUywdnJ0cM6t8eri7O2HvgFABAq9PCwz2pyfk5smeEUqnC9x8/AQCZfNKib89WePPmI8aN6omZUwchS6Z0CXJ/REQRMYlEREREREQUD9KnS4mWzWph8fKt0Ov1WLJiG27ceohjB1fg85uzGDW0C9p2GoH3H74AAC5fuYsWTWuiaOHcAIBCBXKiS8fGOHbiknEq29hR3SGVSuDm5gIvLzc0bVQN509uQPGieRPtPonIfgmcPQvrE7sTREREthBQtUZid4EikeTYkcTuAhFRvBIIBDiybylSpkiGYmWbIzhYiS4dG2PE4M6YPX8dPnz6hqYNq6JcmSJo3HIALly6heTenji0ZzEyZ0qHX/4BcHdzBQDUqNcdFy7dgqdHUrx4dBS37z5G1dpdoFZrkNQ1Cc6eWIfvvj9RqWanRL5rIrI3LKxNREREREQURwP6tEGxInlQrU5XBAcrIRKJMHJoF/TqNxkHj5wFAOzdfxKH9izB2BHdUalmJ3z5+h1Fy7RAhXJF4J3MAzOnDsKdu49x4dItAIBPhjQQiURYumI71GoNAOCXfwD2HTiFHl2aJdatEpEd43Q2IiIiIiKiOChTqhBGDeuKCVOW4dqNBwAAD4+kcHdzxas3H0yOffT4BTKkT2X8WqvV4sSpK/gTrICzkyPGTVpi3Bf0JxgA8ObtR5M2FAoVlCq1rW6HiChSTCIRERERERHFUtYsGbBxzVT8c/wS5i7cYNzu5/cLOp0OBfJlN26TSMSoULYIPn3xNWtnYN922HfwFK7ffGDc9vTZawQEBiFXzswmx5YqUQA3bz+ywd0QEUWN09mIiIiIiIhiQSwWYdOaqYAeWLJiG/LnDUsYvX33CSdOXca0Sf1RqEBO/P4dhIrliyFbVh907zPBpJ1G9asgW5YMaN1hmMl2jUaLxcu2YtyoHtDrdHj5+gMa1quM0iULoG7j3glyj0RE4TGJREREREREFAs5smdEtqw+AIAj+5aa7OvWezw6dB2NkcO6olrlUkiVMhm++fph/JSl2LT1kPE4kUiEkcO6YMWaXXj12nTqGwBMnr4Cv34FoFf3FkibJgU+fv6Gzj3G4uz5G7a9OSIiC7g6GxER/d/i6mx/L67ORkRERPTfw5pIRERERERERERkFZNIRERERBTv3N1csWb5RHx8dRo/Pl7CkX1LkTZNCrPjOrRtgK9vzyN1Km+T7SWK5cORfUvx7d15/HvnAPr2bGWyXyqVYP6sYXjx6Cg+vjqNg7sXI2vm9La8JSIiIrvHJBIRERERxbv1q6agZPH8mD5rNUZPWIic2TNh45ppJsd4eblj3KgemD1/HT58/GrcnjdPVhzcvRjPnr9FnUa9sGDJJowe3g2jh3czHjNyaBcUK5IX3XqPR7vOIyGRiLF/10JIpZIEu0ciIiJ7w8LaRERERBSvypUpjEIFc6Jo6eZ49/4zACAw8A8WzxuFdGlT4u27TwCAaRP749evAMxfvMnk/AF92uLuvSfoN9iQdLp6/T68PN3Rs2tzTJ25EhqNFk0aVsPwMfNw8vRVAMDLV+9x7/oeFMyfA1eu3UvAuyUiIrIfHIlERERERPHq9p1/Ua5Ke2MCCQD8/PwBAFKJ4W+Y5csWQZOGVTF89FwolSqT8/PnzY7T566ZbHv67DWcnBzg6eEGAPBwd4VQKDDul0gMI5AUCtO2iIiIKP5wJBIRERERxavfAUH4HfDaZFvlisXh6+uHF6/eQywWYc70Ifj56zeqVy2NWjXKYtfeEzh1xjCqSKvTwsM9qcn5ObJnhFKpwvcfPwEAZ85dx8C+7XDr9r8I+hOM8aN64PGTV7j34GmC3CMlHq68+ffiyptE//84EomIiIiIbCp9upRo2awWFi/fCr1ejw5tGyBTxrQQi0VIncobJYsXwL4dC9CrWwsAwOUrd9GiaU0ULZwbAFCoQE506dgYx05cgkajBQD07DcJXp5ueHhrH14/PoYihXKjccsB0Ol0iXafRERE/+8Ezp6F9YndCSIiIlvgX6v/Xvxrtf0QCAQ4sm8pUqZIhmJlmyM4WImr57YgZcpkKFWhNd69/wyBQID1q6agepVSyJCtKpydHXFoz2JkzpQOv/wD4O7mCgCoUa87Lly6BQDYuGYqMvmkxaJlWyAQCNCrWws4OMhRtXYXfPn6PTFvmWyMz/a/F5/tRP//OJ2NiIiIiGxmQJ82KFYkD6rV6YrgYCUAIFPGtNiy/bCxZpJer8f6TftRv05FZMuaATdvP0LRMi1QoVwReCfzwMypg3Dn7mNjAilvnqyoWa0ssuSpie/fDdPbDhw6g0d3DqBrp8YYP3lp4twsERHR/zlOZyMiIiIimyhTqhBGDeuKCVOW4dqNB8btQX+C8ebtR5NjFUpDgkmpMhTG1mq1OHHqCv4EK+Ds5Ihxk5YYj83kkxYBgUHGBBJgqMP0/v0XZEif2pa3REREZNeYRCIiIiKieJc1SwZsXDMV/xy/hLkLN5jsu333MXLlzGyyrVTxAvgdEIgnT00Lcg/s2w77Dp7C9ZthSagffr/g7uaK9OlSGrelS5sS2bJmwOfPvja4GyIiAgB3N1esWT4RH1+dxo+Pl3Bk31KkTZPCuP/Zg8MI8L1u8q9D2wZm7SRL5oF3z0+iT4+WZvsy+qTB1An9sGH1VIwa1hUe7q42vSeKGU5nIyIiIqJ4JRaLsGnNVEAPLFmxDfnzZjfue/vuE+Yv2og92+bj9ZuPOH3uGgrlz4kBfdpiwZJNUKs1xmMb1a+CbFkyoHWHYSbt37j1EN++/cDB3Utw4NBpSGVS1K9TETqdDms37E2w+yQisjfrV01BlszpMH3Wami0Wgwd0BEb10x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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "基于 erp_order Schema 生成模拟数据，并按“各区域上半年销量同比”模板画图\n",
    "\"\"\"\n",
    "\n",
    "from faker import Faker\n",
    "from datetime import timedelta\n",
    "import random\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ========== 1. 生成模拟订单数据（基于 erp_order 表结构） ==========\n",
    "\n",
    "def generate_erp_orders():\n",
    "    \"\"\"\n",
    "    按照 erp_order 表结构生成模拟数据：\n",
    "    - 至少 1000 条，这里根据目标销量一共生成 15937 条\n",
    "    - 用省份字段直接存放“区域”信息，方便后续聚合\n",
    "    - 每条订单 quantity=1，这样每个区域的订单条数 = 对应销量\n",
    "    \"\"\"\n",
    "    fake = Faker(\"zh_CN\")\n",
    "    random.seed(42)\n",
    "\n",
    "    # 6 个区域及其目标“销量”（与 Excel 表格完全一致）\n",
    "    region_sales_target = {\n",
    "        \"华北\": 2354,\n",
    "        \"华南\": 1902,\n",
    "        \"东北\": 3524,\n",
    "        \"西北\": 2698,\n",
    "        \"西南\": 2896,\n",
    "        \"华东\": 2563,\n",
    "    }\n",
    "\n",
    "    stores = [\"旗舰店A\", \"旗舰店B\", \"直播店C\", \"线下门店D\"]\n",
    "    platforms = [\"天猫\", \"淘宝\", \"抖音\", \"京东\"]\n",
    "    status_choices = [\"已付款\", \"已发货\", \"已完成\"]\n",
    "    sub_status_choices = [\"已发货\", \"已签收\", \"已完成\"]\n",
    "    refund_status_choices = [\"未申请退款\", \"成功退款\", \"退款关闭\"]\n",
    "\n",
    "    rows = []\n",
    "    cur_id = 1\n",
    "\n",
    "    # 按区域生成订单，每条订单数量=1，使得订单条数之和=销量\n",
    "    for region, sales in region_sales_target.items():\n",
    "        for _ in range(sales):\n",
    "            # 下单、付款、发货时间（保证时间顺序合理）\n",
    "            order_time = fake.date_time_between(start_date=\"-6M\", end_date=\"now\")\n",
    "            payment_date = order_time + timedelta(\n",
    "                minutes=random.randint(5, 180)\n",
    "            )\n",
    "            shipping_date = payment_date + timedelta(\n",
    "                days=random.randint(0, 5)\n",
    "            )\n",
    "\n",
    "            quantity = 1\n",
    "            unit_price = round(random.uniform(80, 300), 2)\n",
    "            product_amount = round(unit_price * quantity, 2)\n",
    "            original_price = round(product_amount * random.uniform(1.05, 1.30), 2)\n",
    "\n",
    "            refund_status = random.choices(\n",
    "                refund_status_choices, weights=[0.7, 0.2, 0.1]\n",
    "            )[0]\n",
    "\n",
    "            # 简单逻辑：未退款则已付金额=商品金额\n",
    "            paid_amount = product_amount if refund_status == \"未申请退款\" else 0\n",
    "\n",
    "            row = {\n",
    "                \"id\": cur_id,\n",
    "                \"internal_order_number\": fake.uuid4(),\n",
    "                \"online_order_number\": fake.uuid4(),\n",
    "                \"store_name\": random.choice(stores),\n",
    "                \"full_channel_user_id\": fake.uuid4().replace(\"-\", \"\")[:16],\n",
    "                \"shipping_date\": shipping_date,\n",
    "                \"payment_date\": payment_date,\n",
    "                \"payable_amount\": product_amount,\n",
    "                \"paid_amount\": paid_amount,\n",
    "                \"status\": random.choice(status_choices),\n",
    "                \"consignee\": fake.name(),\n",
    "                \"spu\": f\"SPU{random.randint(1000, 9999)}\",\n",
    "                \"order_time\": order_time,\n",
    "                # 用 province 字段直接存放“区域”\n",
    "                \"province\": region,\n",
    "                \"city\": fake.city_name(),\n",
    "                \"platform\": random.choice(platforms),\n",
    "                \"sub_order_number\": fake.ean13(),\n",
    "                \"online_sub_order_number\": fake.ean13(),\n",
    "                \"original_online_order_number\": fake.ean13(),\n",
    "                \"sku\": f\"SKU{random.randint(100000, 999999)}\",\n",
    "                \"quantity\": quantity,\n",
    "                \"unit_price\": unit_price,\n",
    "                \"product_name\": random.choice([\"连衣裙\", \"T恤\", \"牛仔裤\", \"外套\"]),\n",
    "                \"color_and_spec\": random.choice(\n",
    "                    [\"黑色 M\", \"白色 L\", \"蓝色 S\", \"卡其 XL\"]\n",
    "                ),\n",
    "                \"product_amount\": product_amount,\n",
    "                \"original_price\": original_price,\n",
    "                \"is_gift\": random.choice([\"是\", \"否\"]),\n",
    "                \"sub_order_status\": random.choice(sub_status_choices),\n",
    "                \"refund_status\": refund_status,\n",
    "                \"registered_quantity\": 0,\n",
    "                \"actual_refund_quantity\": 0,\n",
    "            }\n",
    "\n",
    "            rows.append(row)\n",
    "            cur_id += 1\n",
    "\n",
    "    df_erp = pd.DataFrame(rows)\n",
    "    return df_erp\n",
    "\n",
    "\n",
    "# ==================== 2. 计算“区域销量汇总表”并画图 ====================\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # 1) 生成模拟订单数据\n",
    "    df_erp = generate_erp_orders()\n",
    "    print(\"模拟订单数据条数：\", len(df_erp))\n",
    "\n",
    "    # 2) 按“区域(省份)”统计销量（条数=销量），并构造占位 & 同比去年\n",
    "    regions = [\"华北\", \"华南\", \"东北\", \"西北\", \"西南\", \"华东\"]\n",
    "    # 销量 = 每个区域的订单条数（因为 quantity=1）\n",
    "    sales = (\n",
    "        df_erp.groupby(\"province\")[\"id\"]\n",
    "        .count()\n",
    "        .reindex(regions)\n",
    "        .astype(int)\n",
    "        .values\n",
    "    )\n",
    "\n",
    "    # 同比去年（与截图中的百分比完全一致）\n",
    "    yoy_percent_map = {\n",
    "        \"华北\": 0.12,\n",
    "        \"华南\": 0.25,\n",
    "        \"东北\": 0.16,\n",
    "        \"西北\": 0.21,\n",
    "        \"西南\": 0.18,\n",
    "        \"华东\": 0.25,\n",
    "    }\n",
    "    yoy_percent = [yoy_percent_map[r] for r in regions]\n",
    "\n",
    "    summary = pd.DataFrame({\n",
    "        \"区域\": regions,\n",
    "        \"销量\": sales,\n",
    "        \"占位\": 4500,  # 固定占位值\n",
    "        \"同比去年\": [f\"{int(p*100)}%\" for p in yoy_percent],\n",
    "    })\n",
    "\n",
    "    print(\"\\n汇总结果：\")\n",
    "    print(summary)\n",
    "\n",
    "    # ========== 3. 画图：复刻 Excel 深色背景柱状图 ==========\n",
    "\n",
    "    # 中文字体 & 负号显示\n",
    "    plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "    plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "    x = np.arange(len(regions))\n",
    "    sales_vals = summary[\"销量\"].values\n",
    "    yoy_vals = np.array(yoy_percent)  # 0~1 之间的数用于显示\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "    # 整体深蓝背景\n",
    "    bg_color = \"#0c1435\"\n",
    "    fig.patch.set_facecolor(bg_color)\n",
    "    ax.set_facecolor(bg_color)\n",
    "\n",
    "    # 柱状图（玫红/西瓜红色）\n",
    "    bar_color = \"#ff5b7f\"\n",
    "    bar_width = 0.55\n",
    "    ax.bar(x, sales_vals, width=bar_width, color=bar_color, zorder=2)\n",
    "\n",
    "    # 去掉多余边框，只保留底部轴线\n",
    "    ax.spines[\"top\"].set_visible(False)\n",
    "    ax.spines[\"right\"].set_visible(False)\n",
    "    ax.spines[\"left\"].set_visible(False)\n",
    "    ax.spines[\"bottom\"].set_color(\"#687088\")\n",
    "\n",
    "    ax.tick_params(axis=\"x\", colors=\"white\", labelsize=13)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(regions)\n",
    "\n",
    "    # 不显示 y 轴刻度 & 标签\n",
    "    ax.set_yticks([])\n",
    "    ax.tick_params(axis=\"y\", length=0)\n",
    "\n",
    "    # 设置 y 轴范围，让上方圆圈有足够空间\n",
    "    ymax = sales_vals.max() + 1800\n",
    "    ax.set_ylim(0, ymax)\n",
    "\n",
    "    # 在柱顶显示销量数值\n",
    "    for xi, val in zip(x, sales_vals):\n",
    "        ax.text(\n",
    "            xi,\n",
    "            val + 60,\n",
    "            f\"{val}\",\n",
    "            ha=\"center\",\n",
    "            va=\"bottom\",\n",
    "            color=\"white\",\n",
    "            fontsize=12,\n",
    "        )\n",
    "\n",
    "    # 蓝色圆圈表示“同比去年”\n",
    "    circle_y = sales_vals.max() + 900  # 一条水平线上的圆圈\n",
    "    circle_color = \"#008dff\"\n",
    "    ax.scatter(\n",
    "        x,\n",
    "        [circle_y] * len(x),\n",
    "        s=1800,\n",
    "        color=circle_color,\n",
    "        zorder=3,\n",
    "    )\n",
    "\n",
    "    # 圆圈中的百分比文字\n",
    "    for xi, p in zip(x, yoy_vals):\n",
    "        ax.text(\n",
    "            xi,\n",
    "            circle_y,\n",
    "            f\"{int(p * 100)}%\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            color=\"white\",\n",
    "            fontsize=14,\n",
    "            fontweight=\"bold\",\n",
    "        )\n",
    "\n",
    "    # 主标题 & 副标题\n",
    "    ax.set_title(\n",
    "        \"各区域上半年销量同比\",\n",
    "        fontsize=26,\n",
    "        color=\"white\",\n",
    "        loc=\"left\",\n",
    "        pad=70,\n",
    "    )\n",
    "    fig.text(\n",
    "        0.03,\n",
    "        0.75,\n",
    "        \"东北区域销量持续保持第一，华东和华南同比去年增长最多\",\n",
    "        fontsize=14,\n",
    "        color=\"white\",\n",
    "    )\n",
    "\n",
    "    # 底部数据来源注释\n",
    "    fig.text(\n",
    "        0.05,\n",
    "        0.05,\n",
    "        \"*注：数据来源于公司销售系统，统计日期截至2022.06.30\",\n",
    "        fontsize=10,\n",
    "        color=\"white\",\n",
    "    )\n",
    "\n",
    "    # 调整布局，避免标题和注释被遮挡\n",
    "    plt.tight_layout(rect=[0.02, 0.08, 1, 0.85])\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "088f5527-c185-431d-aed8-bd3c20ec1a81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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